Center for the Multiplexed Assessment of Phenotype

表型多重评估中心

• 批准号: 10563149
• 负责人: Douglas M Fowler
• 金额: $ 254.06万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-05-08 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10563149
• 关键词: Affect; Animal Model; Behavior; Benign; Biological Assay; Cell Line; Cells; Cellular Morphology; Clinic; Clinical; Clinical Data; Code; Communities; Complement; Complex; DNA biosynthesis; Data; Data Scientist; Disease; Education and Outreach; Elements; Expression Library; Family Study; Gene Expression Regulation; Generations; Genes; Genetic; Genetic Transcription; Genetic Variation; Genome; Genomics; Genotype; Goals; Human; Human Genetics; Human Genome; Human Genome Project; In Vitro; Internships; Laboratories; Libraries; Maps; Mass Spectrum Analysis; Measures; Medical; Messenger RNA; Methods; Microscopy; Modeling; Molecular; Molecular Biology; Molecular Profiling; Morphology; Mutagenesis; Mutagens; Mutation; Pathogenicity; Pharmaceutical Preparations; Phenotype; Property; Proteins; Regulatory Element; Reproducibility; Research; Resources; Risk; Single Nucleotide Polymorphism; Site; Solubility; Spliced Genes; Technology; Training; Translating; Universities; Untranslated RNA; Variant; Washington; apprenticeship; career; diagnostic value; environmental stressor; genetic approach; genetic variant; genome sciences; human genome sequencing; innovation; molecular phenotype; mutant; new technology; novel; novel strategies; online resource; prognostic value; protein expression; rare variant; scale up; single cell sequencing; technology development

SUMMARY To date, millions of human genetic variants have been found, many in the coding or regulatory sequence of genes. However, for only a tiny fraction of these variants do we understand how the expression or function of the encoded product is affected. As a consequence, the promise of sequencing human genomes to understand human phenotypes – especially the risk for many diseases with genetic components – has gone largely unfulfilled. What is needed are facile, high-throughput methods for generating libraries of human cells bearing mutant sequence elements and for assessing these libraries to determine each variant's effect on molecular and cellular phenotypes. Thus, the Center for the Multiplexed Assessment of Phenotype, based largely in the University of Washington's Department of Genome Sciences, proposes to develop highly generalizable, reproducible and scalable technologies to generate, and assess the functional impact of, variants in human genes. In the first specific aim, the Center will establish two workhorse methods of mutagenesis to produce variants: saturation editing of genes at their endogenous loci in the human genome, and in vitro generation of variant libraries that are recombined into safe harbor sites. In the second specific aim, the Center will develop approaches to explore the impact of mutations in noncoding regions on versions of genes that have been minimized – pared down to partially remove intronic sequence but still capable of providing essential activity. Further, it will develop mass spectrometry methods to analyze variation in coding sequences for its effect on protein abundance, stability, interactions, turnover and aggregation. In the third specific aim, the Center will assess variant effects on cell morphology, behavior and internal organization by using a novel, microscopy- based phenotyping technology, and on global transcription by developing a massively parallel single-cell mRNA profiling method. Center-developed technologies will be piloted on a set of human genes with disease relevance, enabling comparisons between each variant's functional effects and the effects of known pathogenic or benign variants. This effort will inform the use in the clinic of the large-scale functional data the Center's technologies will generate. Additionally, variants will be assessed under different conditions, such as in multiple cell lines, in combination with another mutation, or in the presence of a drug. The Center will also train early career experimentalists, clinical geneticists and data scientists to obtain and use large-scale functional data. This training will include internships in Center laboratories for one to three months, and apprenticeships for one to two years. These close interactions will generate medically- and biologically-relevant results and reveal the best paths for translating the vast amounts of Center-generated functional data for clinical use. Through these new technologies and their dissemination to the broader clinical community, the Center will advance the promise of the Human Genome Project by interpreting the vast landscape of human genetic variation.

总结 到目前为止，已经发现了数百万种人类遗传变异，其中许多是在基因的编码或调控序列中。 基因.然而，对于这些变体中的一小部分，我们了解了它们的表达或功能， 编码产品受到影响。因此，对人类基因组进行测序以了解 人类的表型--特别是许多具有遗传成分的疾病的风险--已经大大降低， 没有实现所需要的是用于产生携带人细胞的文库的容易的、高通量的方法。 突变体序列元件，并用于评估这些文库以确定每个变体对分子和 细胞表型因此，表型多重评估中心，主要基于 华盛顿大学的基因组科学系，建议开发高度通用， 可重复和可扩展的技术，以产生和评估人类基因组中变异体的功能影响。 基因.在第一个具体目标中，该中心将建立两种诱变方法， 变体：在人类基因组中内源基因座处的基因饱和编辑，以及体外产生 变异文库被重组到安全港位点。在第二个具体目标中，中心将发展 探索非编码区突变对已被发现的基因版本的影响的方法 最小化- 此外，它还将开发质谱分析方法，以分析编码序列的变化对 蛋白质丰度、稳定性、相互作用、周转和聚集。在第三个具体目标中，中心将 通过使用一种新的显微镜，评估对细胞形态、行为和内部组织的不同影响， 基于表型分析技术，并通过开发大规模平行的单细胞mRNA 剖析法中心开发的技术将在一组与疾病相关的人类基因上进行试验， 使每个变体的功能效应与已知的致病或良性的效应之间能够进行比较， 变体。这项工作将告知临床使用的大规模功能数据中心的技术 将产生。此外，将在不同条件下评估变体，例如在多个细胞系中， 与另一种突变的组合，或在药物的存在下。本中心还将进行职业生涯早期培训 实验学家，临床遗传学家和数据科学家获得和使用大规模的功能数据。这 培训将包括在中心实验室实习一至三个月， 两年这些密切的相互作用将产生医学和生物学相关的结果，并揭示最好的 翻译中心生成的大量功能数据以供临床使用的途径。通过这些新 技术及其传播到更广泛的临床社区，该中心将推进承诺， 人类基因组计划通过解释人类遗传变异的广阔前景。

项目成果

Comparable specimen collection from both ends of at-home mid-turbinate swabs.

从家用中鼻甲拭子两端采集的可比样本。

• DOI: 10.1101/2020.12.05.20244632
• 发表时间: 2020
• 期刊: medRxiv : the preprint server for health sciences
• 作者: Truong,Melissa;Pfau,Brian;McDermot,Evan;Han,PeterD;Brandstetter,Elisabeth;Richardson,Matthew;Kim,AshleyE;Rieder,MarkJ;Chu,HelenY;Englund,JanetA;Nickerson,DeborahA;Shendure,Jay;Lockwood,ChristinaM;Konnick,EricQ;Starita,
• 通讯作者: Starita,

fqfa: A pure Python package for genomic sequence files.

fqfa：基因组序列文件的纯 Python 包。

• DOI: 10.21105/joss.02076
• 发表时间: 2020
• 期刊: Journal of open source software
• 作者: Rubin,AlanF